Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

Yuemin Zheng, Jin Tao, Hao Sun, Qinglin Sun, Zengqiang Chen, Matthias Dehmer and Quan Zhou
Additional contact information
Yuemin Zheng: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Jin Tao: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Hao Sun: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Qinglin Sun: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Zengqiang Chen: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Matthias Dehmer: Department of Computer Science, Swiss Distance University of Applied Sciences, 3900 Brig, Switzerland
Quan Zhou: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland

Energies, 2021, vol. 14, issue 16, 1-17

Abstract: To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.

Keywords: load frequency control; linear active disturbance rejection control; soft actor-critic; multi-source power system; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/16/4804/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/16/4804/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:16:p:4804-:d:610068

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().